CWAS-Plus: Estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data.

Variants in cis-regulatory elements link the noncoding genome to human brain pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS) employs both whole-genome sequencing and user-provided functional data to enhance noncoding variant analysis, with a faster and more efficient execution of the CWAS workflow. Here, we used single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type specific enhancers and promoters. Examining autism spectrum disorder whole-genome sequencing data (n = 7,280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease whole-genome sequencing data (n = 1,087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale whole-genome sequencing data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.

Short tandem repeat expansions in cortical layer-specific genes implicate in phenotypic severity and adaptability of autism spectrum disorder.

AIM: Short tandem repeats (STRs) are repetitive DNA sequences and highly mutable in various human disorders. While the involvement of STRs in various genetic disorders has been extensively studied, their role in autism spectrum disorder (ASD) remains largely unexplored. In this study, we aimed to investigate genetic association of STR expansions with ASD using whole genome sequencing (WGS) and identify risk loci associated with ASD phenotypes. METHODS: We analyzed WGS data of 634 ASD families and performed genome-wide evaluation for 12,929 STR loci. We found rare STR expansions that exceeded normal repeat lengths in autism cases compared to unaffected controls. By integrating single cell RNA and ATAC sequencing datasets of human postmortem brains, we prioritized STR loci in genes specifically expressed in cortical development stages. A deep learning method was used to predict functionality of ASD-associated STR loci. RESULTS: In ASD cases, rare STR expansions predominantly occurred in early cortical layer-specific genes involved in neurodevelopment, highlighting the cellular specificity of STR-associated genes in ASD risk. Leveraging deep learning prediction models, we demonstrated that these STR expansions disrupted the regulatory activity of enhancers and promoters, suggesting a potential mechanism through which they contribute to ASD pathogenesis. We found that individuals with ASD-associated STR expansions exhibited more severe ASD phenotypes and diminished adaptability compared to non-carriers. CONCLUSION: Short tandem repeat expansions in cortical layer-specific genes are associated with ASD and could potentially be a risk genetic factor for ASD. Our study is the first to show evidence of STR expansion associated with ASD in an under-investigated population.

Data-mining analysis of media frame effects on social perception of schizophrenia renaming in Korea.

BACKGROUND: In 2011, Korean Neuropsychiatric Association renamed schizophrenia from 'mind split disorder' ('Jungshinbunyeolbyung' in Korean) to 'attunement disorder' ('Johyeonbyung' in Korean), in a strategic way to reduce social stigma toward people with schizophrenia. However, there remains an elusive consensus that how the renaming effort has contributed to changes in the social perception of schizophrenia in Korea. METHODS: With this regard, we explored whether media frames alter the social perception, in ways of respecting or disrespecting schizophrenia patients before and after the renaming. This study extensively investigated media keywords related to schizophrenia across the time by applying both language and epidemiologic analyses. RESULTS: In results, the media keywords have been negatively described for schizophrenia patients both before and after the renaming. Further, from an analysis using the regression model, a significant correlation was observed between the frequency of negative keywords and the hospitalization frequency of schizophrenia patients. CONCLUSIONS: These findings suggest that the social perception of schizophrenia has been scarcely changed, but rather remained negatively biased against schizophrenia patients, in spite of the renaming effort. Notably, the biased media frames have been demonstrated to negatively impact on the social perception, and even on the medical use patterns of general schizophrenia patients. In conclusion, we suggest that the unbiased media frames along with the renaming effort may collectively help reduce the negative social perception of schizophrenia. TRIAL REGISTRATION: This study was approved from the Institute of Review Board (IRB) of the Yoing-In Mental Hospital (IRB No. YIMH-IRB-2019-02).

Microbiota-Gut-Brain Axis in Major Depression: A New Therapeutic Approach.

Major depression is impacted by the disruption of gut microbiota. Defects in gut microbiota can lead to microbiota-gut-brain axis dysfunction and increased vulnerability to major depression. While traditional chemotherapeutic approaches, such as antidepressant use, produce an overall partial therapeutic effect on depression, the gut microbiome has emerged as an effective target for better therapeutic outcomes. Recent representative studies on the microbiota hypothesis to explore the association between gut pathophysiology and major depression have indicated that restoring gut microbiota and microbiota-gut-brain axis could alleviate depression. We reviewed studies that supported the gut microbiota hypothesis to better understand the pathophysiology of depression; we also explored reports suggesting that gut microbiota restoration is an effective approach for improving depression. These findings indicate that gut microbiota and microbiota-gut-brain axis are appropriate new therapeutic targets for major depression.

Risks of complicated acute appendicitis in patients with psychiatric disorders.

BACKGROUND: Acute appendicitis often presents with vague abdominal pain, which fosters diagnostic challenges to clinicians regarding early detection and proper intervention. This is even more problematic with individuals with severe psychiatric disorders who have reduced sensitivity to pain due to long-term or excessive medication use or disturbed bodily sensation perceptions. This study aimed to determine whether psychiatric disorder, psychotropic prescription, and treatment compliance increase the risks of complicated acute appendicitis. METHODS: The diagnosis records of acute appendicitis from four university hospitals in Korea were investigated from 2002 to 2020. A total of 47,500 acute appendicitis-affected participants were divided into groups with complicated and uncomplicated appendicitis to determine whether any of the groups had more cases of psychiatric disorder diagnoses. Further, the ratio of complicated compared to uncomplicated appendicitis in the mentally ill group was calculated regarding psychotropic dose, prescription duration, and treatment compliance. RESULTS: After adjusting for age and sex, presence of psychotic disorder (odds ratio [OR]: 1.951; 95% confidence interval [CI]: 1.218-3.125), and bipolar disorder (OR: 2.323; 95% CI: 1.194-4.520) was associated with a higher risk of having complicated appendicitis compared with absence of psychiatric disorders. Patients who are taking high-daily-dose antipsychotics, regardless of prescription duration, show high complicated appendicitis risks; High-dose antipsychotics for < 1 year (OR: 1.896, 95% CI: 1.077-3.338), high-dose antipsychotics for 1-5 years (OR: 1.930, 95% CI: 1.144-3.256). Poor psychiatric outpatient compliance was associated with a high risk of complicated appendicitis (OR: 1.664, 95% CI: 1.014-2.732). CONCLUSIONS: This study revealed a close relationship in the possibility of complicated appendicitis in patients with severe psychiatric disorders, including psychotic and bipolar disorders. The effect on complicated appendicitis was more remarkable by the psychiatric disease entity itself than by psychotropic prescription patterns. Good treatment compliance and regular visit may reduce the morbidity of complicated appendicitis in patients with psychiatric disorders.

Analysis of low-level somatic mosaicism reveals stage and tissue-specific mutational features in human development.

Most somatic mutations that arise during normal development are present at low levels in single or multiple tissues depending on the developmental stage and affected organs. However, the effect of human developmental stages or mutations of different organs on the features of somatic mutations is still unclear. Here, we performed a systemic and comprehensive analysis of low-level somatic mutations using deep whole-exome sequencing (average read depth ~500×) of 498 multiple organ tissues with matched controls from 190 individuals. Our results showed that early clone-forming mutations shared between multiple organs were lower in number but showed higher allele frequencies than late clone-forming mutations [0.54 vs. 5.83 variants per individual; 6.17% vs. 1.5% variant allele frequency (VAF)] along with less nonsynonymous mutations and lower functional impacts. Additionally, early and late clone-forming mutations had unique mutational signatures that were distinct from mutations that originated from tumors. Compared with early clone-forming mutations that showed a clock-like signature across all organs or tissues studied, late clone-forming mutations showed organ, tissue, and cell-type specificity in the mutation counts, VAFs, and mutational signatures. In particular, analysis of brain somatic mutations showed a bimodal occurrence and temporal-lobe-specific signature. These findings provide new insights into the features of somatic mosaicism that are dependent on developmental stage and brain regions.

The Relationship between Stress, Inflammation, and Depression.

A narrative review about the relationship between stress, inflammation, and depression is made as follows: Chronic stress leads to various stress-related diseases such as depression. Although most human diseases are related to stress exposure, the common pathways between stress and pathophysiological processes of different disorders are still debatable. Chronic inflammation is a crucial component of chronic diseases, including depression. Both experimental and clinical studies have demonstrated that an increase in the levels of pro-inflammatory cytokines and stress hormones, such as glucocorticoids, substantially contributes to the behavioral alterations associated with depression. Evidence suggests that inflammation plays a key role in the pathology of stress-related diseases; however, this link has not yet been completely explored. In this study, we aimed to determine the role of inflammation in stress-induced diseases and whether a common pathway for depression exists. Recent studies support pharmacological and non-pharmacological treatment approaches significantly associated with ameliorating depression-related inflammation. In addition, major depression can be associated with an activated immune system, whereas antidepressants can exert immunomodulatory effects. Moreover, non-pharmacological treatments for major depression (i.e., exercise) may be mediated by anti-inflammatory actions. This narrative review highlights the mechanisms underlying inflammation and provides new insights into the prevention and treatment of stress-related diseases, particularly depression.

Non-coding de novo mutations in chromatin interactions are implicated in autism spectrum disorder.

Three-dimensional chromatin interactions regulate gene expressions. The significance of de novo mutations (DNMs) in chromatin interactions remains poorly understood for autism spectrum disorder (ASD). We generated 813 whole-genome sequences from 242 Korean simplex families to detect DNMs, and identified target genes which were putatively affected by non-coding DNMs in chromatin interactions. Non-coding DNMs in chromatin interactions were significantly involved in transcriptional dysregulations related to ASD risk. Correspondingly, target genes showed spatiotemporal expressions relevant to ASD in developing brains and enrichment in biological pathways implicated in ASD, such as histone modification. Regarding clinical features of ASD, non-coding DNMs in chromatin interactions particularly contributed to low intelligence quotient levels in ASD probands. We further validated our findings using two replication cohorts, Simons Simplex Collection (SSC) and MSSNG, and showed the consistent enrichment of non-coding DNM-disrupted chromatin interactions in ASD probands. Generating human induced pluripotent stem cells in two ASD families, we were able to demonstrate that non-coding DNMs in chromatin interactions alter the expression of target genes at the stage of early neural development. Taken together, our findings indicate that non-coding DNMs in ASD probands lead to early neurodevelopmental disruption implicated in ASD risk via chromatin interactions.

The Entorhinal Cortex and Adult Neurogenesis in Major Depression.

Depression is characterized by impairments in adult neurogenesis. Reduced hippocampal function, which is suggestive of neurogenesis impairments, is associated with depression-related phenotypes. As adult neurogenesis operates in an activity-dependent manner, disruption of hippocampal neurogenesis in depression may be a consequence of neural circuitry impairments. In particular, the entorhinal cortex is known to have a regulatory effect on the neural circuitry related to hippocampal function and adult neurogenesis. However, a comprehensive understanding of how disruption of the neural circuitry can lead to neurogenesis impairments in depression remains unclear with respect to the regulatory role of the entorhinal cortex. This review highlights recent findings suggesting neural circuitry-regulated neurogenesis, with a focus on the potential role of the entorhinal cortex in hippocampal neurogenesis in depression-related cognitive and emotional phenotypes. Taken together, these findings may provide a better understanding of the entorhinal cortex-regulated hippocampal neurogenesis model of depression.

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome.

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, "a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed" as the "next-generation treatment for mental disorders" by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

Low-Level Brain Somatic Mutations Are Implicated in Schizophrenia.

BACKGROUND: Somatic mutations arising from the brain have recently emerged as significant contributors to neurodevelopmental disorders, including childhood intractable epilepsy and cortical malformations. However, whether brain somatic mutations are implicated in schizophrenia (SCZ) is not well established. METHODS: We performed deep whole exome sequencing (average read depth > 550×) of matched dorsolateral prefrontal cortex and peripheral tissues from 27 patients with SCZ and 31 age-matched control individuals, followed by comprehensive and strict analysis of somatic mutations, including mutagenesis signature, substitution patterns, and involved pathways. In particular, we explored the impact of deleterious mutations in GRIN2B through primary neural culture. RESULTS: We identified an average of 4.9 and 5.6 somatic mutations per exome per brain in patients with SCZ and control individuals, respectively. These mutations presented with average variant allele frequencies of 8.0% in patients with SCZ and 7.6% in control individuals. Although mutational profiles, such as the number and type of mutations, showed no significant difference between patients with SCZ and control individuals, somatic mutations in SCZ brains were significantly enriched for SCZ-related pathways, including dopamine receptor, glutamate receptor, and long-term potentiation pathways. Furthermore, we showed that brain somatic mutations in GRIN2B (encoding glutamate ionotropic NMDA receptor subunit 2B), which were found in two patients with SCZ, disrupted the location of GRIN2B across the surface of dendrites among primary cultured neurons. CONCLUSIONS: Taken together, this study shows that brain somatic mutations are associated with the pathogenesis of SCZ.

Neural Circuitry-Neurogenesis Coupling Model of Depression.

Depression is characterized by the disruption of both neural circuitry and neurogenesis. Defects in hippocampal activity and volume, indicative of reduced neurogenesis, are associated with depression-related behaviors in both humans and animals. Neurogenesis in adulthood is considered an activity-dependent process; therefore, hippocampal neurogenesis defects in depression can be a result of defective neural circuitry activity. However, the mechanistic understanding of how defective neural circuitry can induce neurogenesis defects in depression remains unclear. This review highlights the current findings supporting the neural circuitry-regulated neurogenesis, especially focusing on hippocampal neurogenesis regulated by the entorhinal cortex, with regard to memory, pattern separation, and mood. Taken together, these findings may pave the way for future progress in neural circuitry-neurogenesis coupling studies of depression.

Brain somatic mutations observed in Alzheimer's disease associated with aging and dysregulation of tau phosphorylation.

The role of brain somatic mutations in Alzheimer's disease (AD) is not well understood. Here, we perform deep whole-exome sequencing (average read depth 584×) in 111 postmortem hippocampal formation and matched blood samples from 52 patients with AD and 11 individuals not affected by AD. The number of somatic single nucleotide variations (SNVs) in AD brain specimens increases significantly with aging, and the rate of mutation accumulation in the brain is 4.8-fold slower than that in AD blood. The putatively pathogenic brain somatic mutations identified in 26.9% (14 of 52) of AD individuals are enriched in PI3K-AKT, MAPK, and AMPK pathway genes known to contribute to hyperphosphorylation of tau. We show that a pathogenic brain somatic mutation in PIN1 leads to a loss-of-function mutation. In vitro mimicking of haploinsufficiency of PIN1 aberrantly increases tau phosphorylation and aggregation. This study provides new insights into the genetic architecture underlying the pathogenesis of AD.

Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning.

OBJECTIVE: The aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD. METHODS: We used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted p value ＜0.05) and were further analyzed in a test dataset (n=16, 8 ASD cases and 8 controls) using machine learning algorithms. RESULTS: Hierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively. CONCLUSION: The results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy.